The present invention relates to the field of image processing, and in particular to image processing in medical applications. In particular, the present invention is directed to a deep-leaning based method for motion estimation and motion compensation of a helical computed tomography (CT) scan of an object of interest.
Motion is one of the most critical sources of artifacts in helical cone-beam computed tomography (CT). Motion artifacts result from discrepancies between the requirement that the object remain unchanged during the scan and reality, in which the object changes (“deforms” or “moves”) during the scan. Motion of the patient, whether voluntary or involuntary, during image acquisition may result in motion artifacts in the reconstructed image. Involuntary motion, such as respiration or cardiac motion, may result in motion artifacts. While there are techniques known in the art for the reconstruction of motion compensated images from circular CT scan data, the known techniques do not address the unique conditions of a helical CT scan, wherein the field of view (FOV) is continuously changing.
Accordingly, what is needed in the art is an improved system and method for motion estimation and compensating for motion by reducing motion artifacts produced during image reconstruction from helical computed tomography (CT) scan data.